Introducing STEM Concepts: How Can STEM or STEAM Be Effectively Integrated into the Kindergarten Curriculum?
Bibliographic record
Abstract
This study explores the integration of STEM/STEAM (Science, Technology, Engineering, Art, and Mathematics) concepts into the kindergarten curriculum, focusing on the perceptions, readiness, and challenges experienced by pre-service teachers at Kuwait University. Using a mixed-methods approach, data were collected from 350 participants through a structured questionnaire combining Likert-scale items and open-ended questions.The quantitative results revealed strong agreement on the value of STEM/STEAM in enhancing children's creativity, problem-solving skills, and academic readiness. However, participants also reported significant challenges, including limited access to educational resources, time constraints within the curriculum, and a need for more practical training. Thematic analysis of qualitative responses confirmed these findings, highlighting participants’ desire for hands-on workshops, technological tools, and institutional support.The study concludes that while future educators are motivated to adopt interdisciplinary learning methods, effective implementation requires systemic changes in teacher preparation programs, resource allocation, and classroom scheduling. Recommendations are provided for curriculum developers, policymakers, and educational institutions to foster more accessible and impactful STEM/STEAM education in early childhood settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".